Endometrial carcinoma (EC) is a highly heterogeneous disease entity, consisting of at least two categories defined by distinct histopathological and clinical features. In addition to the traditionally defined type I (association with hyperestrogenism, low grade endometrioid histology, and good prognosis) and type II (no well defined epidemiologic risk factors, high grade papillary serous or clear cell histologies, and poor prognosis) tumors, there may also exist a poorly defined third category, consisting of endometrioid tumors of high architectural or nuclear grade and moderate to poor prognosis. Often it is difficult to classify an individual case of EC with respect to one or another of these general categories. This heterogeneity in clinical outcome presumably reflects differences in the underlying molecular genetic characteristics of individual cancers, but the molecular genetic basis of EC remains largely obscure. Thus, the long term goal of this project is to address the hypothesis that a comprehensive molecular genetic classification of EC will improve our ability to classify individual cases of EC and predict response to therapy and clinical outcome. Furthermore, elucidation of the molecular determinants of these clinical outcome parameters should provide substantial insights into the biological basis of endometrial tumorigenesis, as well as suggest potential new targets or strategies for EC therapy. We propose that this goal may be accomplished through the systematic application of new, comprehensive molecular screening methodologies, specifically, the use of microarrays for genome-wide gene expression analyses, together with a large EC tissue resource linked to extensive surgical, histopathological, and clinical data. The specific aims of this project are to: 1) obtain RNA expression profiles of a large number of ECs using oligonucleotide expression arrays to identify subgroups assembled by expression characteristics; 2) define distinct molecular subsets of EC that will predict response to therapy and survival, by correlating these molecular observations with clinical outcomes and pathological states; and 3) confirm and refine observations from specific aims one and two using targeted assays and tissue-based approaches.